Bibliographic record
Abstract
The experience of low- and middle-income countries (LMC) with respect to regulation and legislation in the health sector is in marked contrast to that of Canada and Europe. It is suggested that the degree to which regulatory mechanisms can influence private sector activity in LMC is quite low. However, there has been little work done on exploring just how, and to what extent, these regulations fail. Through the use of stakeholder interviews, this study explored the effectiveness of regulations directed at the private-for-profit sector (general practitioners, private clinics and hospitals) in Zimbabwe. The study found that there was limited and asymmetric knowledge of basic regulations among government bodies and private providers. However, there was a clear feeling that regulations are not being implemented and enforced effectively. A variety of opportunistic practices have been observed among private providers, including: practices of self-referral, where patients are sent to other services the provider has a financial interest in; over-servicing; doctor-patient collusion to collect health insurance payments; and the use of unlicensed staff in private facilities. Key factors limiting effectiveness of regulation in the health sector include the over-centralization and lack of independence of the regulatory body, the absence of legal mechanisms to control the price of care, and the lack of knowledge by patients of their rights. The study also identified a number of potential strategies for improving the current regulatory environment. For example, in order to improve monitoring, 'informal' arrangements between the centralized regulatory body and local authorities developed. There is a need to develop ways to formalize the role of these authorities. In addition, professional associations of private providers are also identified as key players through which to improve the impact of regulation among private providers. Increasing consumer access to information and knowledge is another potential way to improve information within the regulatory process as well as implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".